





'''simple docstring'''






import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
					import os

					import jax.numpy as jnp
					from jax import jit

					from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
					from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

					lowerCAmelCase									=   """0.12"""  # assumed parallelism: 8


if is_torch_available():
					import torch






def   __A    (				a_    :	Optional[Any]   ,a_    :	Dict   ,a_    :	Optional[int]=None						):
		if rng is None:
				lowerCAmelCase  :     Tuple       						= random.Random()

		lowerCAmelCase  :     Optional[int]       						= 1
		for dim in shape:
				total_dims *= dim

		lowerCAmelCase  :     List[str]       						= []
		for _ in range(a_						):
				values.append(rng.randint(0   ,vocab_size - 1						)						)

		lowerCAmelCase  :     Tuple       						= np.array(a_   ,dtype=jnp.intaa						).reshape(a_						)

		return output






def   __A    (				a_    :	Optional[Any]   ,a_    :	Union[str, Any]=None						):
		lowerCAmelCase  :     str       						= ids_tensor(a_   ,vocab_size=2   ,rng=a_						)
		# make sure that at least one token is attended to for each batch
		lowerCAmelCase  :     Dict       						= 1
		return attn_mask
@require_flax
class 		lowerCamelCase	:
	snake_case_   = None
	snake_case_   = ()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

			# cut to half length & take max batch_size 3
			lowerCAmelCase  :     Dict       						= 2
			lowerCAmelCase  :     str       						= inputs["input_ids"].shape[-1] // 2
			lowerCAmelCase  :     Tuple       						= inputs["input_ids"][:max_batch_size, :sequence_length]

			lowerCAmelCase  :     str       						= jnp.ones_like(a_						)
			lowerCAmelCase  :     Union[str, Any]       						= attention_mask[:max_batch_size, :sequence_length]

			# generate max 5 tokens
			lowerCAmelCase  :     int       						= input_ids.shape[-1] + 5
			if config.eos_token_id is not None and config.pad_token_id is None:
					# hack to allow generate for models such as GPT2 as is done in `generate()`
					lowerCAmelCase  :     Tuple       						= config.eos_token_id
			return config, input_ids, attention_mask, max_length



	@is_pt_flax_cross_test
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Any       						= self._get_input_ids_and_config()
			lowerCAmelCase  :     List[Any]       						= False
			lowerCAmelCase  :     Optional[Any]       						= max_length
			lowerCAmelCase  :     Tuple       						= 0

			for model_class in self.all_generative_model_classes:
					lowerCAmelCase  :     List[str]       						= model_class(a_						)

					lowerCAmelCase  :     int       						= model_class.__name__[4:]  # Skip the "Flax" at the beginning
					lowerCAmelCase  :     Any       						= getattr(a_		,   a_						)
					lowerCAmelCase  :     str       						= pt_model_class(a_						).eval()
					lowerCAmelCase  :     str       						= load_flax_weights_in_pytorch_model(a_		,   flax_model.params						)

					lowerCAmelCase  :     Dict       						= flax_model.generate(a_						).sequences
					lowerCAmelCase  :     str       						= pt_model.generate(torch.tensor(a_		,   dtype=torch.long						)						)

					if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
							lowerCAmelCase  :     Optional[int]       						= flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

					self.assertListEqual(pt_generation_outputs.numpy().tolist()		,   flax_generation_outputs.tolist()						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     List[str]       						= self._get_input_ids_and_config()
			lowerCAmelCase  :     int       						= False
			lowerCAmelCase  :     int       						= max_length

			for model_class in self.all_generative_model_classes:
					lowerCAmelCase  :     int       						= model_class(a_						)

					lowerCAmelCase  :     Dict       						= model.generate(a_						).sequences
					self.assertEqual(generation_outputs.shape[-1]		,   a_						)

					lowerCAmelCase  :     str       						= jit(model.generate						)
					lowerCAmelCase  :     Dict       						= jit_generate(a_						).sequences

					self.assertListEqual(generation_outputs.tolist()		,   jit_generation_outputs.tolist()						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     List[str]       						= self._get_input_ids_and_config()
			lowerCAmelCase  :     Any       						= True
			lowerCAmelCase  :     Optional[Any]       						= max_length

			for model_class in self.all_generative_model_classes:
					lowerCAmelCase  :     List[Any]       						= model_class(a_						)

					lowerCAmelCase  :     Tuple       						= model.generate(a_						).sequences
					self.assertEqual(generation_outputs.shape[-1]		,   a_						)

					lowerCAmelCase  :     Dict       						= jit(model.generate						)
					lowerCAmelCase  :     Dict       						= jit_generate(a_						).sequences

					self.assertListEqual(generation_outputs.tolist()		,   jit_generation_outputs.tolist()						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= self._get_input_ids_and_config()
			lowerCAmelCase  :     Any       						= False
			lowerCAmelCase  :     Dict       						= max_length
			lowerCAmelCase  :     Union[str, Any]       						= 2

			for model_class in self.all_generative_model_classes:
					lowerCAmelCase  :     List[str]       						= model_class(a_						)

					lowerCAmelCase  :     Optional[int]       						= model.generate(a_						).sequences
					self.assertEqual(generation_outputs.shape[-1]		,   a_						)

					lowerCAmelCase  :     List[str]       						= jit(model.generate						)
					lowerCAmelCase  :     List[str]       						= jit_generate(a_						).sequences

					self.assertListEqual(generation_outputs.tolist()		,   jit_generation_outputs.tolist()						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Dict       						= self._get_input_ids_and_config()
			lowerCAmelCase  :     int       						= False
			lowerCAmelCase  :     Dict       						= max_length
			lowerCAmelCase  :     Dict       						= 2
			lowerCAmelCase  :     Optional[int]       						= 2

			for model_class in self.all_generative_model_classes:
					lowerCAmelCase  :     Any       						= model_class(a_						)

					lowerCAmelCase  :     Optional[int]       						= model.generate(a_						).sequences
					self.assertEqual(generation_outputs.shape[0]		,   input_ids.shape[0] * config.num_return_sequences						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     str       						= self._get_input_ids_and_config()
			lowerCAmelCase  :     Tuple       						= True
			lowerCAmelCase  :     str       						= max_length
			lowerCAmelCase  :     Union[str, Any]       						= 0.8
			lowerCAmelCase  :     List[Any]       						= 10
			lowerCAmelCase  :     Tuple       						= 0.3
			lowerCAmelCase  :     Any       						= 1
			lowerCAmelCase  :     str       						= 8
			lowerCAmelCase  :     List[Any]       						= 9

			for model_class in self.all_generative_model_classes:
					lowerCAmelCase  :     str       						= model_class(a_						)

					lowerCAmelCase  :     str       						= model.generate(a_						).sequences
					self.assertEqual(generation_outputs.shape[-1]		,   a_						)

					lowerCAmelCase  :     List[str]       						= jit(model.generate						)
					lowerCAmelCase  :     int       						= jit_generate(a_						).sequences

					self.assertListEqual(generation_outputs.tolist()		,   jit_generation_outputs.tolist()						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     str       						= self._get_input_ids_and_config()
			lowerCAmelCase  :     List[str]       						= max_length
			lowerCAmelCase  :     Optional[Any]       						= 1
			lowerCAmelCase  :     Any       						= 8
			lowerCAmelCase  :     Any       						= 9

			for model_class in self.all_generative_model_classes:
					lowerCAmelCase  :     Tuple       						= model_class(a_						)

					lowerCAmelCase  :     str       						= model.generate(a_						).sequences
					self.assertEqual(generation_outputs.shape[-1]		,   a_						)

					lowerCAmelCase  :     Optional[int]       						= jit(model.generate						)
					lowerCAmelCase  :     Optional[int]       						= jit_generate(a_						).sequences

					self.assertListEqual(generation_outputs.tolist()		,   jit_generation_outputs.tolist()						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Any       						= self._get_input_ids_and_config()
			lowerCAmelCase  :     Tuple       						= max_length
			lowerCAmelCase  :     Dict       						= 2
			lowerCAmelCase  :     str       						= 1
			lowerCAmelCase  :     Tuple       						= 8
			lowerCAmelCase  :     str       						= 9

			for model_class in self.all_generative_model_classes:
					lowerCAmelCase  :     Dict       						= model_class(a_						)

					lowerCAmelCase  :     List[Any]       						= model.generate(a_						).sequences
					self.assertEqual(generation_outputs.shape[-1]		,   a_						)

					lowerCAmelCase  :     List[str]       						= jit(model.generate						)
					lowerCAmelCase  :     Union[str, Any]       						= jit_generate(a_						).sequences

					self.assertListEqual(generation_outputs.tolist()		,   jit_generation_outputs.tolist()						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Tuple       						= self._get_input_ids_and_config()

			# pad attention mask on the left
			lowerCAmelCase  :     List[Any]       						= attention_mask.at[(0, 0)].set(0						)

			lowerCAmelCase  :     Tuple       						= False
			lowerCAmelCase  :     List[str]       						= max_length

			for model_class in self.all_generative_model_classes:
					lowerCAmelCase  :     str       						= model_class(a_						)

					lowerCAmelCase  :     Optional[int]       						= model.generate(a_		,   attention_mask=a_						).sequences
					self.assertEqual(generation_outputs.shape[-1]		,   a_						)

					lowerCAmelCase  :     Union[str, Any]       						= jit(model.generate						)
					lowerCAmelCase  :     Tuple       						= jit_generate(a_		,   attention_mask=a_						).sequences

					self.assertListEqual(generation_outputs.tolist()		,   jit_generation_outputs.tolist()						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Dict       						= self._get_input_ids_and_config()

			# pad attention mask on the left
			lowerCAmelCase  :     Union[str, Any]       						= attention_mask.at[(0, 0)].set(0						)

			lowerCAmelCase  :     str       						= True
			lowerCAmelCase  :     Dict       						= max_length

			for model_class in self.all_generative_model_classes:
					lowerCAmelCase  :     Tuple       						= model_class(a_						)

					lowerCAmelCase  :     int       						= model.generate(a_		,   attention_mask=a_						).sequences
					self.assertEqual(generation_outputs.shape[-1]		,   a_						)

					lowerCAmelCase  :     int       						= jit(model.generate						)
					lowerCAmelCase  :     Tuple       						= jit_generate(a_		,   attention_mask=a_						).sequences

					self.assertListEqual(generation_outputs.tolist()		,   jit_generation_outputs.tolist()						)





	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= self._get_input_ids_and_config()

			# pad attention mask on the left
			lowerCAmelCase  :     Optional[int]       						= attention_mask.at[(0, 0)].set(0						)

			lowerCAmelCase  :     List[Any]       						= 2
			lowerCAmelCase  :     str       						= max_length

			for model_class in self.all_generative_model_classes:
					lowerCAmelCase  :     Optional[Any]       						= model_class(a_						)

					lowerCAmelCase  :     Any       						= model.generate(a_		,   attention_mask=a_						).sequences
					self.assertEqual(generation_outputs.shape[-1]		,   a_						)

					lowerCAmelCase  :     List[str]       						= jit(model.generate						)
					lowerCAmelCase  :     Optional[Any]       						= jit_generate(a_		,   attention_mask=a_						).sequences

					self.assertListEqual(generation_outputs.tolist()		,   jit_generation_outputs.tolist()						)






@require_flax
class 		lowerCamelCase	(   unittest.TestCase							):



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"						)
			lowerCAmelCase  :     List[str]       						= FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"						)

			lowerCAmelCase  :     Optional[int]       						= "Hello world"
			lowerCAmelCase  :     Any       						= tokenizer(a_		,   return_tensors="np"						).input_ids

			# typos are quickly detected (the correct argument is `do_sample`)
			with self.assertRaisesRegex(a_		,   "do_samples"						):
					model.generate(a_		,   do_samples=a_						)

			# arbitrary arguments that will not be used anywhere are also not accepted
			with self.assertRaisesRegex(a_		,   "foo"						):
					lowerCAmelCase  :     Optional[int]       						= {"foo": "bar"}
					model.generate(a_		,   **a_						)










'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase									=   {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase									=   [
					    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MraForMaskedLM""",
					    """MraForMultipleChoice""",
					    """MraForQuestionAnswering""",
					    """MraForSequenceClassification""",
					    """MraForTokenClassification""",
					    """MraLayer""",
					    """MraModel""",
					    """MraPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mra import (
										    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MraForMaskedLM,
										    MraForMultipleChoice,
										    MraForQuestionAnswering,
										    MraForSequenceClassification,
										    MraForTokenClassification,
										    MraLayer,
										    MraModel,
										    MraPreTrainedModel,
										)
else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure)



